diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 6b6718ab419491566715de020fdce46d324a288b..4841dce7581ea26f92bdaf8a556ef80762ff6f4d 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -90,11 +90,11 @@ n_varobs = size(options_.varobs,1);
%% Set priors over the estimated parameters.
if ~isempty(estim_params_)
- [xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
+ [xparam1,estim_params_,bayestopt_,lb,ub] = set_prior(estim_params_);
if any(bayestopt_.pshape > 0)
% Plot prior densities.
if options_.mode_compute
- plot_priors(bayestopt_,M_,options_)
+ plot_priors
end
% Set prior bounds
bounds = prior_bounds(bayestopt_);
@@ -255,21 +255,21 @@ if ~isreal(rawdata)
error('There are complex values in the data! Probably a wrong transformation')
end
% Test for missing observations.
-options_.missing_data = any(any(isnan(rawdata)));
+%options_.missing_data = any(any(isnan(rawdata)));
% Prefilter the data if needed.
if options_.prefilter == 1
- if options_.missing_data
- bayestopt_.mean_varobs = zeros(n_varobs,1);
- for variable=1:n_varobs
- rdx = find(~isnan(rawdata(:,variable)));
- m = mean(rawdata(rdx,variable));
- rawdata(rdx,variable) = rawdata(rdx,variable)-m;
- bayestopt_.mean_varobs(variable) = m;
- end
- else
+ % if options_.missing_data
+ % bayestopt_.mean_varobs = zeros(n_varobs,1);
+ % for variable=1:n_varobs
+ % rdx = find(~isnan(rawdata(:,variable)));
+ % m = mean(rawdata(rdx,variable));
+ % rawdata(rdx,variable) = rawdata(rdx,variable)-m;
+ % bayestopt_.mean_varobs(variable) = m;
+ % end
+ %else
bayestopt_.mean_varobs = mean(rawdata,1)';
rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
- end
+ %end
end
% Transpose the dataset array.
data = transpose(rawdata);
@@ -302,9 +302,9 @@ end
%% compute sample moments if needed (bvar-dsge)
if options_.bvar_dsge
- if options_.missing_data
- error('I cannot estimate a DSGE-VAR model with missing observations!')
- end
+ % if options_.missing_data
+ % error('I cannot estimate a DSGE-VAR model with missing observations!')
+ %end
if options_.noconstant
evalin('base',...
['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
@@ -319,14 +319,14 @@ if options_.bvar_dsge
end
end
-[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
-missing_value = ~(number_of_observations == gend*n_varobs);
+%[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
+%missing_value = ~(number_of_observations == gend*n_varobs);
-initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+initial_estimation_checks(xparam1,gend,data);
if options_.mode_compute == 0 & length(options_.mode_file) == 0
if options_.smoother == 1
- [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
+ [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
oo_.Smoother.integration_order = d;
@@ -366,7 +366,7 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
end
if ~options_.bvar_dsge
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
- fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend,data);
else
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend);
@@ -379,7 +379,7 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
if ~options_.bvar_dsge
- [xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ [xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend,data);
else
[xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend);
end
@@ -390,9 +390,9 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
verbose = 2;
if ~options_.bvar_dsge
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
- csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,gend,data);
disp(sprintf('Objective function at mode: %f',fval))
- disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)))
+ disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data)))
else
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('DsgeVarLikelihood',xparam1,H0,[],crit,nit,options_.gradient_method,gend);
@@ -421,14 +421,14 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
nit=1000;
end
if ~options_.bvar_dsge
- [xparam1,hh,gg,fval,invhess] = newrat('DsgeLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ [xparam1,hh,gg,fval,invhess] = newrat('DsgeLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend,data);
else
[xparam1,hh,gg,fval,invhess] = newrat('DsgeVarLikelihood',xparam1,hh,gg,igg,crit,nit,flag,gend);
end
save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','bayestopt_');
case 6
if ~options_.bvar_dsge
- fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ fval = DsgeLikelihood(xparam1,gend,data);
else
fval = DsgeVarLikelihood(xparam1,gend);
end
@@ -466,9 +466,8 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
end
if ~options_.bvar_dsge
[xparam1,PostVar,Scale,PostMean] = ...
- gmhmaxlik('DsgeLikelihood',xparam1,bounds,options_.Opt6Numb,Scale,flag,MeanPar,CovJump,gend,data,...
- data_index,number_of_observations,no_more_missing_observations);
- fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ gmhmaxlik('DsgeLikelihood',xparam1,bounds,options_.Opt6Numb,Scale,flag,MeanPar,CovJump,gend,data);
+ fval = DsgeLikelihood(xparam1,gend,data);
else
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik('DsgeVarLikelihood',xparam1,bounds,options_.Opt6Numb,Scale,flag,MeanPar,CovJump,gend);
@@ -489,8 +488,8 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
if ~options_.bvar_dsge
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik('DsgeLikelihood',xparam1,bounds,...
- options_.Opt6Numb,Scale,flag,PostMean,PostVar,gend,data,data_index,number_of_observations,no_more_missing_observations);
- fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ options_.Opt6Numb,Scale,flag,PostMean,PostVar,gend,data);
+ fval = DsgeLikelihood(xparam1,gend,data);
else
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik('DsgeVarLikelihood',xparam1,bounds,...
@@ -499,7 +498,7 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
end
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
- fval = DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ fval = DsgeLikelihood(xparam1,gend,data);
disp(['Change in the covariance matrix = ' num2str(mouvement) '.'])
disp(['Mode improvement = ' num2str(abs(OldMode-fval))])
OldMode = fval;
@@ -513,7 +512,7 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
if ~options_.bvar_dsge
- [xparam1,fval,exitflag] = fminsearch(fh,xparam1,optim_options,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ [xparam1,fval,exitflag] = fminsearch(fh,xparam1,optim_options,gend,data);
else
[xparam1,fval,exitflag] = fminsearch(fh,xparam1,optim_options,gend);
end
@@ -523,9 +522,9 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
if options_.mode_compute ~= 5
if options_.mode_compute ~= 6
if ~options_.bvar_dsge
- hh = reshape(hessian('DsgeLikelihood',xparam1,options_.gstep,gend,data,data_index,number_of_observations,no_more_missing_observations),nx,nx);
+ hh = reshape(hessian('DsgeLikelihood',xparam1,gend,data),nx,nx);
else
- hh = reshape(hessian('DsgeVarLikelihood',xparam1,options_.gstep,gend),nx,nx);
+ hh = reshape(hessian('DsgeVarLikelihood',xparam1,gend),nx,nx);
end
end
save([M_.fname '_mode.mat'],'xparam1','hh','bayestopt_');
@@ -533,7 +532,7 @@ if options_.mode_compute > 0 & options_.posterior_mode_estimation
end
if options_.mode_check == 1 & options_.posterior_mode_estimation
- mode_check(xparam1,0,hh,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
+ mode_check(xparam1,0,hh,gend,data,lb,ub);
end
if options_.posterior_mode_estimation
@@ -647,7 +646,7 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
if ~options_.bvar_dsge
md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
- - DsgeLikelihood(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
+ - DsgeLikelihood(xparam1,gend,data);
else
md_Laplace = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess ...
- DsgeVarLikelihood(xparam1,gend);
@@ -943,8 +942,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
if options_.bvar_dsge
feval(options_.posterior_sampling_method,'DsgeVarLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend);
else
- feval(options_.posterior_sampling_method,'DsgeLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend,data,...
- data_index,number_of_observations,no_more_missing_observations);
+ feval(options_.posterior_sampling_method,'DsgeLikelihood',options_.proposal_distribution,xparam1,invhess,bounds,gend,data);
end
end
if ~options_.nodiagnostic & options_.mh_replic > 1000 & options_.mh_nblck > 1
@@ -966,7 +964,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
oo_ = compute_moments_varendo(options_,M_,oo_,var_list_);
end
if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
- prior_posterior_statistics('posterior',data,gend,data_index,missing_value);
+ prior_posterior_statistics('posterior',data);
end
xparam = get_posterior_parameters('mean');
set_all_parameters(xparam);
@@ -976,7 +974,7 @@ if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape
> 0) & options_.load_mh_file)) ...
| ~options_.smoother ) & M_.endo_nbr^2*gend < 1e7 % to be fixed
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
- [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
+ [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
oo_.Smoother.integration_order = d;